This proposal is in response to the Symptoms of Lower Urinary Tract Dysfunction Research Network (LURN) (U01) (RFA-DK-11-026) Funding Opportunity Announcement. Lower urinary tract dysfunction (LUTD) is found in many pathologic conditions. Measurement tools that are not disease-specific can help better characterize symptoms of LUTD across conditions; potentially linking those symptoms to genetic variants and clinical subtypes. This in turn can improve care for patients with LUTD. New measurement tools need to be expanded to include men and woman alike. Effective LUTD symptom reporting can help better characterize urology patients, more effectively guide treatment, and clarify relationships between phenotype and biological substrates (e.g., genotype, neurophysiology). This is an important aim of the NIDDK's Prostate Strategic Plan. Existing self-report tools measure different symptoms with varying reliability and validity; this proposal will redress inconsistencies and deficiencies found in existing measures of urinary symptoms. Using methods we developed in PROMIS and related projects, we propose to develop a state-of-the art self-report measure of LUTD as well as their associated features and common comorbidities. Because our newly-created self-report measure will employ item response theory (IRT)-calibrated item banks where possible, we will create both static and adaptive, computer-based versions of the test. Computer adaptive testing (CAT) allows for brief-yet-precise measurement, reducing patient burden. Measures that are responsive to treatment impact are needed for clinical research and clinical practice aimed to manage symptoms of LUTD. Our methods include two aims: Instrument development/calibration; and clinical validation. To develop and calibrate the instrument, we will conduct two phases of qualitative research with approximately 40 urology patients, conduct a systematic and comprehensive literature review, collect input from 15 experts, and conduct state-of-the-science psychometric analysis of data collected from an Internet-based study of 800 individuals with LUTD, sleep disorder, obesity, or healthy controls. We will evaluate item response theory parameters, convergent validity with established self-report measures, and the relationships among LUTD and two emerging risks for LUTD: obesity and sleep disorders. We will then validate our new tools against conventionally-used questionnaires (e.g., International Prostate Scoring System), known biometric correlates of LUTD, such as urinary flow rate (Qmax), and newly-identified candidate genes for LUTD. Specifically, we will examine the ability of our new scale, and existing scales, to detect differences across, and associations within, four groups: obesity (n = 64), sleep dysfunction (n = 64), people with a history of LUTD (n = 160); and healthy controls (n = 64). This phase of the project will provide information about patient phenotype, and advance our knowledge on genotype-phenotype relationships.